Overview

Dataset statistics

Number of variables15
Number of observations20811
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory153.4 B

Variable types

Numeric10
Categorical5

Alerts

IND_ESTADO_INACTIVA has constant value "0"Constant
CNT_CUENTAS_DISTINTAS is highly imbalanced (93.9%)Imbalance
IND_DEBAJO_UMBRAL_15K is highly imbalanced (78.0%)Imbalance
IND_SUP_15K is highly imbalanced (93.9%)Imbalance
CNT_CAJEROS_DISTINTOS is highly imbalanced (78.2%)Imbalance
PORC_RETIRO is highly skewed (γ1 = 53.22548984)Skewed
NUM_AUTORIZACION_TC has unique valuesUnique

Reproduction

Analysis started2023-04-08 22:07:30.583425
Analysis finished2023-04-08 22:07:53.511682
Duration22.93 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

NUM_AUTORIZACION_TC
Real number (ℝ)

Distinct20811
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872610.3
Minimum804821
Maximum938008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:53.688504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum804821
5-th percentile812684
Q1839491
median872871
Q3906917.5
95-th percentile930746
Maximum938008
Range133187
Interquartile range (IQR)67426.5

Descriptive statistics

Standard deviation38410.661
Coefficient of variation (CV)0.044018116
Kurtosis-1.2607981
Mean872610.3
Median Absolute Deviation (MAD)33698
Skewness-0.02217849
Sum1.8159893 × 1010
Variance1.4753789 × 109
MonotonicityNot monotonic
2023-04-08T22:07:53.907736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
886862 1
 
< 0.1%
847331 1
 
< 0.1%
912398 1
 
< 0.1%
906171 1
 
< 0.1%
872366 1
 
< 0.1%
829111 1
 
< 0.1%
816607 1
 
< 0.1%
933277 1
 
< 0.1%
808414 1
 
< 0.1%
932275 1
 
< 0.1%
Other values (20801) 20801
> 99.9%
ValueCountFrequency (%)
804821 1
< 0.1%
804825 1
< 0.1%
804844 1
< 0.1%
804847 1
< 0.1%
804861 1
< 0.1%
804871 1
< 0.1%
804877 1
< 0.1%
804879 1
< 0.1%
804905 1
< 0.1%
804914 1
< 0.1%
ValueCountFrequency (%)
938008 1
< 0.1%
938004 1
< 0.1%
938002 1
< 0.1%
937989 1
< 0.1%
937977 1
< 0.1%
937963 1
< 0.1%
937951 1
< 0.1%
937948 1
< 0.1%
937939 1
< 0.1%
937929 1
< 0.1%
Distinct99
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439.19453
Minimum50
Maximum4759.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:54.123178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile100
Q1100
median200
Q3500
95-th percentile1500
Maximum4759.22
Range4709.22
Interquartile range (IQR)400

Descriptive statistics

Standard deviation518.2961
Coefficient of variation (CV)1.180106
Kurtosis11.98296
Mean439.19453
Median Absolute Deviation (MAD)100
Skewness2.9123285
Sum9140077.4
Variance268630.85
MonotonicityNot monotonic
2023-04-08T22:07:54.304764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6647
31.9%
200 4066
19.5%
300 2660
12.8%
500 2468
 
11.9%
1000 1959
 
9.4%
2000 643
 
3.1%
400 549
 
2.6%
600 348
 
1.7%
700 244
 
1.2%
800 219
 
1.1%
Other values (89) 1008
 
4.8%
ValueCountFrequency (%)
50 61
 
0.3%
100 6647
31.9%
113.12 1
 
< 0.1%
115.78 1
 
< 0.1%
150 10
 
< 0.1%
163.62 1
 
< 0.1%
200 4066
19.5%
250 3
 
< 0.1%
300 2660
12.8%
350 2
 
< 0.1%
ValueCountFrequency (%)
4759.22 1
 
< 0.1%
4757.58 1
 
< 0.1%
4753.51 1
 
< 0.1%
4749.12 1
 
< 0.1%
4747.71 2
 
< 0.1%
4745.68 1
 
< 0.1%
4723.37 1
 
< 0.1%
4721.88 1
 
< 0.1%
4693.15 1
 
< 0.1%
3937.77 5
< 0.1%

NUM_CTA_DEB
Real number (ℝ)

Distinct1615
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28612.863
Minimum82
Maximum47919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:54.477618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile2813.5
Q119752
median30710
Q339259
95-th percentile46170
Maximum47919
Range47837
Interquartile range (IQR)19507

Descriptive statistics

Standard deviation13017.102
Coefficient of variation (CV)0.45493882
Kurtosis-0.70765986
Mean28612.863
Median Absolute Deviation (MAD)9128
Skewness-0.51297123
Sum5.9546229 × 108
Variance1.6944495 × 108
MonotonicityNot monotonic
2023-04-08T22:07:54.667248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416 58
 
0.3%
407 48
 
0.2%
20773 40
 
0.2%
25710 39
 
0.2%
4702 36
 
0.2%
19423 35
 
0.2%
22356 33
 
0.2%
39904 31
 
0.1%
36245 31
 
0.1%
11921 31
 
0.1%
Other values (1605) 20429
98.2%
ValueCountFrequency (%)
82 11
 
0.1%
281 11
 
0.1%
407 48
0.2%
412 16
 
0.1%
416 58
0.3%
472 16
 
0.1%
485 6
 
< 0.1%
491 14
 
0.1%
584 11
 
0.1%
590 15
 
0.1%
ValueCountFrequency (%)
47919 11
0.1%
47847 11
0.1%
47819 12
0.1%
47788 15
0.1%
47777 2
 
< 0.1%
47775 12
0.1%
47750 2
 
< 0.1%
47748 16
0.1%
47746 14
0.1%
47717 12
0.1%

hora
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.805343
Minimum0
Maximum23
Zeros34
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:54.838244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111
median14
Q317
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1027905
Coefficient of variation (CV)0.29718859
Kurtosis-0.67218048
Mean13.805343
Median Absolute Deviation (MAD)3
Skewness-0.17473104
Sum287303
Variance16.83289
MonotonicityNot monotonic
2023-04-08T22:07:55.015359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 1854
 
8.9%
17 1822
 
8.8%
12 1762
 
8.5%
13 1760
 
8.5%
11 1588
 
7.6%
16 1478
 
7.1%
10 1449
 
7.0%
19 1448
 
7.0%
15 1366
 
6.6%
14 1289
 
6.2%
Other values (14) 4995
24.0%
ValueCountFrequency (%)
0 34
 
0.2%
1 29
 
0.1%
2 16
 
0.1%
3 22
 
0.1%
4 27
 
0.1%
5 101
 
0.5%
6 382
 
1.8%
7 638
3.1%
8 993
4.8%
9 1277
6.1%
ValueCountFrequency (%)
23 60
 
0.3%
22 127
 
0.6%
21 390
 
1.9%
20 899
4.3%
19 1448
7.0%
18 1854
8.9%
17 1822
8.8%
16 1478
7.1%
15 1366
6.6%
14 1289
6.2%

PAIS_ORIGEN
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.878237
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:55.155828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile37
Q137
median37
Q337
95-th percentile37
Maximum49
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6857582
Coefficient of variation (CV)0.045711462
Kurtosis224.98653
Mean36.878237
Median Absolute Deviation (MAD)0
Skewness-13.312534
Sum767473
Variance2.8417806
MonotonicityNot monotonic
2023-04-08T22:07:55.281927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37 20616
99.1%
22 134
 
0.6%
2 20
 
0.1%
49 15
 
0.1%
40 12
 
0.1%
41 11
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
2 20
 
0.1%
9 3
 
< 0.1%
22 134
 
0.6%
37 20616
99.1%
40 12
 
0.1%
41 11
 
0.1%
49 15
 
0.1%
ValueCountFrequency (%)
49 15
 
0.1%
41 11
 
0.1%
40 12
 
0.1%
37 20616
99.1%
22 134
 
0.6%
9 3
 
< 0.1%
2 20
 
0.1%

COD_MONEDA_ORIGEN
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.541204
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:55.408546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q129
median29
Q329
95-th percentile29
Maximum34
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1120776
Coefficient of variation (CV)0.10903806
Kurtosis47.174467
Mean28.541204
Median Absolute Deviation (MAD)0
Skewness-6.9169319
Sum593971
Variance9.6850273
MonotonicityNot monotonic
2023-04-08T22:07:55.525711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
29 20310
97.6%
6 344
 
1.7%
19 124
 
0.6%
1 14
 
0.1%
34 10
 
< 0.1%
23 9
 
< 0.1%
ValueCountFrequency (%)
1 14
 
0.1%
6 344
 
1.7%
19 124
 
0.6%
23 9
 
< 0.1%
29 20310
97.6%
34 10
 
< 0.1%
ValueCountFrequency (%)
34 10
 
< 0.1%
29 20310
97.6%
23 9
 
< 0.1%
19 124
 
0.6%
6 344
 
1.7%
1 14
 
0.1%

COD_PROD_EMISOR
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1769737
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:56.014969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.3988663
Coefficient of variation (CV)0.65653537
Kurtosis-1.4434622
Mean5.1769737
Median Absolute Deviation (MAD)2
Skewness0.55913269
Sum107738
Variance11.552292
MonotonicityNot monotonic
2023-04-08T22:07:56.137205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 8021
38.5%
10 6440
30.9%
4 5233
25.1%
6 405
 
1.9%
7 361
 
1.7%
5 188
 
0.9%
3 152
 
0.7%
1 11
 
0.1%
ValueCountFrequency (%)
1 11
 
0.1%
2 8021
38.5%
3 152
 
0.7%
4 5233
25.1%
5 188
 
0.9%
6 405
 
1.9%
7 361
 
1.7%
10 6440
30.9%
ValueCountFrequency (%)
10 6440
30.9%
7 361
 
1.7%
6 405
 
1.9%
5 188
 
0.9%
4 5233
25.1%
3 152
 
0.7%
2 8021
38.5%
1 11
 
0.1%

TIP_NEGOCIO
Real number (ℝ)

Distinct2158
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2615.0127
Minimum1
Maximum5699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:56.296760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile244
Q11475
median2796
Q33957
95-th percentile4660
Maximum5699
Range5698
Interquartile range (IQR)2482

Descriptive statistics

Standard deviation1476.7668
Coefficient of variation (CV)0.56472645
Kurtosis-1.1428807
Mean2615.0127
Median Absolute Deviation (MAD)1192
Skewness-0.19338403
Sum54421030
Variance2180840.3
MonotonicityNot monotonic
2023-04-08T22:07:56.478064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3988 828
 
4.0%
123 238
 
1.1%
4012 228
 
1.1%
4011 227
 
1.1%
4007 202
 
1.0%
946 177
 
0.9%
4225 173
 
0.8%
2867 91
 
0.4%
3783 82
 
0.4%
4009 79
 
0.4%
Other values (2148) 18486
88.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
4 3
 
< 0.1%
5 12
0.1%
16 23
0.1%
19 5
 
< 0.1%
21 21
0.1%
23 9
 
< 0.1%
24 7
 
< 0.1%
25 5
 
< 0.1%
26 20
0.1%
ValueCountFrequency (%)
5699 21
0.1%
5648 2
 
< 0.1%
5647 15
0.1%
5640 19
0.1%
5485 15
0.1%
5476 1
 
< 0.1%
5462 5
 
< 0.1%
5461 7
 
< 0.1%
5433 3
 
< 0.1%
5432 3
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.2 KiB
0
20811 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20811
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20811
100.0%

Length

2023-04-08T22:07:56.649313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:56.820672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20811
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20811
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20811
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20811
100.0%

PORC_RETIRO
Real number (ℝ)

Distinct2203
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.05503
Minimum0
Maximum200000
Zeros176
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:56.974971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.1
median0.29
Q30.98
95-th percentile93.09
Maximum200000
Range200000
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation3274.3999
Coefficient of variation (CV)25.975957
Kurtosis3044.9434
Mean126.05503
Median Absolute Deviation (MAD)0.24
Skewness53.22549
Sum2623331.1
Variance10721695
MonotonicityNot monotonic
2023-04-08T22:07:57.179145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 712
 
3.4%
1 627
 
3.0%
0.01 589
 
2.8%
0.04 576
 
2.8%
0.03 569
 
2.7%
0.06 517
 
2.5%
0.05 516
 
2.5%
0.07 476
 
2.3%
0.1 447
 
2.1%
0.08 419
 
2.0%
Other values (2193) 15363
73.8%
ValueCountFrequency (%)
0 176
 
0.8%
0.01 589
2.8%
0.02 712
3.4%
0.03 569
2.7%
0.04 576
2.8%
0.05 516
2.5%
0.06 517
2.5%
0.07 476
2.3%
0.08 419
2.0%
0.09 418
2.0%
ValueCountFrequency (%)
200000 4
< 0.1%
150000 1
 
< 0.1%
130000 1
 
< 0.1%
100000 1
 
< 0.1%
60000 1
 
< 0.1%
30000 3
< 0.1%
25000 1
 
< 0.1%
20000 1
 
< 0.1%
18181.82 6
< 0.1%
16666.67 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.2 KiB
1
20551 
2
 
259
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20811
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%

Length

2023-04-08T22:07:57.359634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:57.516084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20551
98.8%
2 259
 
1.2%
3 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.2 KiB
0
20076 
1
 
735

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

Length

2023-04-08T22:07:57.645561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:57.794721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20076
96.5%
1 735
 
3.5%

IND_SUP_15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.2 KiB
0
20664 
1
 
147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

Length

2023-04-08T22:07:57.919227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:58.091545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 20811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20664
99.3%
1 147
 
0.7%

CNT_RETIRO_CUENTA
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.224785
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size325.2 KiB
2023-04-08T22:07:58.202282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54699634
Coefficient of variation (CV)0.44660602
Kurtosis29.15112
Mean1.224785
Median Absolute Deviation (MAD)0
Skewness3.878639
Sum25489
Variance0.29920499
MonotonicityNot monotonic
2023-04-08T22:07:58.329356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 16990
81.6%
2 3208
 
15.4%
3 460
 
2.2%
4 110
 
0.5%
5 24
 
0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
1 16990
81.6%
2 3208
 
15.4%
3 460
 
2.2%
4 110
 
0.5%
5 24
 
0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 7
 
< 0.1%
5 24
 
0.1%
4 110
 
0.5%
3 460
 
2.2%
2 3208
15.4%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.2 KiB
1
18766 
2
1884 
3
 
145
4
 
15
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20811
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Length

2023-04-08T22:07:58.471307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:58.649266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 20811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18766
90.2%
2 1884
 
9.1%
3 145
 
0.7%
4 15
 
0.1%
5 1
 
< 0.1%

Interactions

2023-04-08T22:07:49.545609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:31.281981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:33.803530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:35.520288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:38.045018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:40.694697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.371973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:44.151991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.843074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.775638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:49.756052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:31.485726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:33.978824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:35.722851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:38.352175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:40.864394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.567822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:44.328042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:46.018140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.969842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:50.040129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:31.661845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.138812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:35.885394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:38.661281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.037690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.729364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:44.487995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:46.173698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:48.135130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:50.289249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:31.844058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.303823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:36.048981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:38.983590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.202197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.899671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:44.672967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:46.339932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:48.307893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:50.567029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:32.019675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.480418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:36.294006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:39.310467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.358055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.058341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:44.824200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:46.496297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:48.467836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:50.848958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:32.204283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.652144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:36.554248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:39.630745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.505729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.228883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.000246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:46.936188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:48.644487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:51.173420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:32.406576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.825778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:36.851896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:39.812027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.689439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.408075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.174645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.113552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:48.843668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:51.446398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:33.244397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:34.984541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:37.150708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:39.964826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:41.867882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.572313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.335339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.267853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:49.005744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:51.737720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:33.429899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:35.150048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:37.441449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:40.351413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.037668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.772113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.500676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.427389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:49.183871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:52.047623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:33.633795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:35.346751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:37.764115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:40.522472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:42.218143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:43.972578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:45.687708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:47.614149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:49.373584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-08T22:07:52.490829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-08T22:07:53.158220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NUM_AUTORIZACION_TCMON_MOVIMIENTO_QUETZALIZADONUM_CTA_DEBhoraPAIS_ORIGENCOD_MONEDA_ORIGENCOD_PROD_EMISORTIP_NEGOCIOIND_ESTADO_INACTIVAPORC_RETIROCNT_CUENTAS_DISTINTASIND_DEBAJO_UMBRAL_15KIND_SUP_15KCNT_RETIRO_CUENTACNT_CAJEROS_DISTINTOS
COD_CLIENTE
38792886862200.046584837291069600.0510011
10487890641200.034338183729257000.1910011
23357864199300.0148061837291063905.2710011
8889875335200.04762083729256700.2210021
38008845043100.0447391037296380600.0610011
26625849303100.0246881237294231600.1810011
22765867380600.0240281237294323300.2110011
31476860090500.024714737294199900.0210011
4315858690500.043412143729262300.0910022
20130894430300.0394801337294371304.1510011
NUM_AUTORIZACION_TCMON_MOVIMIENTO_QUETZALIZADONUM_CTA_DEBhoraPAIS_ORIGENCOD_MONEDA_ORIGENCOD_PROD_EMISORTIP_NEGOCIOIND_ESTADO_INACTIVAPORC_RETIROCNT_CUENTAS_DISTINTASIND_DEBAJO_UMBRAL_15KIND_SUP_15KCNT_RETIRO_CUENTACNT_CAJEROS_DISTINTOS
COD_CLIENTE
39003918266700.01978613372910304303.4610011
12922932405700.03571520372922948035.5510011
294719301901000.03624514372910163401.2610011
301878508001000.0410821737291019400.0610011
18308901102200.0277691937292171700.7110011
24124853870200.0319771237292397001.8510011
428018328091000.019352173729494200.0210021
371228674332000.0429491237294212800.5911011
18267883573100.0387171337294398800.0910011
21659856951200.039466937294284800.2210011